Current State and Challenges of Physiologically Based Biopharmaceutics Modeling (PBBM) in Oral Drug Product Development

Integrating Clinical Variability into PBPK Models for Virtual Bioequivalence of Single and Multiple Doses of Tofacitinib Modified-Release Dosage Form

Tofacitinib is a potent, selective inhibitor of the Janus kinase (JAK) family of kinases with a high degree of selectivity within the human genome's set of protein kinases. Currently approved formulations for tofacitinib citrate are immediate-release (IR) tablets, modified-release (MR) tablets, and IR solution. A once daily MR microsphere formulation was developed for use in pediatric patients. Demonstration of bioequivalence (BE) between the 10 mg once daily (q.d.) MR microsphere formulation and 5 mg twice daily (b.i.d.) IR solution is needed to enable the exposure-response analyses-based bridging to support regulatory approval. To assess BE between MR microsphere and IR solution, an innovative approach was utilized with physiologically-based pharmacokinetic (PBPK) virtual BE trials (VBE) in lieu of a clinical BE trial. A PBPK model was developed to characterize the absorption of different formulations of tofacitinib using Simcyp ADAM module. VBE trials were conducted by simulating PK profiles using the verified PBPK model and integrating the clinically observed intrasubject coefficient of variation (ICV) where BE was assessed with a predetermined sample size and prespecified criteria. The VBE trials demonstrated BE between IR solution 5 mg b.i.d. and MR microsphere 10 mg q.d. after a single dose on day 1 and after multiple doses on day 5. This research presents an innovative approach that incorporates clinically observed ICV in PBPK model-based VBE trials, which could reduce unnecessary drug exposure to healthy volunteers and streamline new formulation development strategies.

Understanding the mechanisms of food effect on omaveloxolone pharmacokinetics through physiologically based biopharmaceutics modeling

Omaveloxolone is a nuclear factor (erythroid-derived 2)-like 2 activator approved in the United States and the European Union for the treatment of patients with Friedreich ataxia aged ≥16 years, with a recommended dosage of 150 mg orally once daily on an empty stomach. The effect of the US Food and Drug Administration (FDA) high-fat breakfast on the pharmacokinetic profile of omaveloxolone observed in study 408-C-1703 (NCT03664453) deviated from the usual linear correlation between fed/fasted maximum plasma concentration (Cmax) and area under the concentration-time curve (AUC) ratios reported for various oral drugs across 323 food effect studies. Here, physiologically based biopharmaceutics modeling (PBBM) was implemented to predict and explain the effect of the FDA high-fat breakfast on a 150-mg dose of omaveloxolone. The model was developed and validated based on dissolution and pharmacokinetic data available across dose-ranging, food effect, and drug-drug interaction clinical studies. PBBM predictions support clinical observations of the unique effect of a high-fat meal on omaveloxolone pharmacokinetic profile, in which the Cmax increased by 350% with only a 15% increase in the AUC. Key parameters influencing omaveloxolone pharmacokinetics in the fasted state based on a parameter sensitivity analysis included bile salt solubilization, CYP3A4 activity, drug substance particle size distribution, and permeability. Mechanistically, in vivo omaveloxolone absorption was solubility and dissolution rate limited. However, in the fed state, higher bile salt solubilization led to more rapid dissolution with predominant absorption in the upper gastrointestinal tract, resulting in increased susceptibility to first-pass gut extraction; this accounts for the lack of correlation between Cmax and AUC for omaveloxolone.

Dose Optimization Informed by PBPK Modeling: State-of-the Art and Future

Model-informed drug development (MIDD) is a powerful quantitative approach that plays an integral role in drug development and regulatory review. While applied throughout the life cycle of the development of new drugs, a key application of MIDD is to inform clinical trial design including dose selection and optimization. To date, physiologically-based pharmacokinetic (PBPK) modeling, an established component of the MIDD toolkit, has mainly been used for assessment of drug-drug interactions (DDIs) and consequential dose adjustments in regulatory submissions. As a result of recent scientific advances and growing confidence in the utility of the approach, PBPK models are being increasingly applied to provide dose recommendations for subjects with differing ages, genetics, and disease states. In this review, we present our perspective on the current landscape of regulatory acceptance of PBPK applications supported by relevant case studies. We also discuss the recent progress and future challenges associated with expanding the utility of PBPK models into emerging areas for regulatory decision making, especially dose optimization in highly vulnerable and understudied populations and facilitating diversity in clinical trials.

More Than a Gut Feeling─A Combination of Physiologically Driven Dissolution and Pharmacokinetic Modeling as a Tool for Understanding Human Gastric Motility

In vivo studies of formulation performance with in vitro and/or in silico simulations are often limited by significant gaps in our knowledge of the interaction between administered dosage forms and the human gastrointestinal tract. This work presents a novel approach for the investigation of gastric motility influence on dosage form performance, by combining biopredictive dissolution tests in an innovative PhysioCell apparatus with mechanistic physiology-based pharmacokinetic modeling. The methodology was based on the pharmacokinetic data from a large (n = 118) cohort of healthy volunteers who ingested a capsule containing a highly soluble and rapidly absorbed drug under fasted conditions. The developed dissolution tests included biorelevant media, varied fluid flows, and mechanical stress events of physiological timing and intensity. The dissolution results were used as inputs for pharmacokinetic modeling that led to the deduction of five patterns of gastric motility and their prevalence in the studied population. As these patterns significantly influenced the observed pharmacokinetic profiles, the proposed methodology is potentially useful to other in vitro-in vivo predictions involving immediate-release oral dosage forms.

PBPK-led assessment of antimalarial drugs as candidates for Covid-19: Simulating concentrations at the site of action to inform repurposing strategies

The urgent need for safe, efficacious, and accessible drug treatments to treat coronavirus disease 2019 (COVID-19) prompted a global effort to evaluate drug repurposing opportunities. Pyronaridine and amodiaquine are both components of approved antimalarials with in vitro activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In vitro activity does not always translate to clinical efficacy across a therapeutic dose range. This study applied available, verified, physiologically based pharmacokinetic (PBPK) models for pyronaridine, amodiaquine, and its active metabolite N-desethylamodiaquine (DEAQ) to predict drug concentrations in lung tissue relative to plasma or blood in the default healthy virtual population. Lung exposures were compared to published data across the reported range of in vitro EC50 values against SARS-CoV-2. In the multicompartment permeability-limited PBPK model, the predicted total Cmax in lung mass for pyronaridine was 34.2 μM on Day 3, 30.5-fold greater than in blood (1.12 μM) and for amodiaquine was 0.530 μM, 8.83-fold greater than in plasma (0.060 μM). In the perfusion-limited PBPK model, the DEAQ predicted total Cmax on Day 3 in lung mass (30.2 μM) was 21.4-fold greater than for plasma (1.41 μM). Based on the available in vitro data, predicted drug concentrations in lung tissue for pyronaridine and DEAQ, but not amodiaquine, appeared sufficient to inhibit SARS-CoV-2 replication. Simulations indicated standard dosing regimens of pyronaridine-artesunate and artesunate-amodiaquine have potential to treat COVID-19. These findings informed repurposing strategies to select the most relevant compounds for clinical investigation in COVID-19. Clinical data for model verification may become available from ongoing clinical studies.

Assessing and mitigating pH-mediated DDI risks in drug development - formulation approaches and clinical considerations

pH-mediated drug-drug interactions (DDI) is a prevalent DDI in drug development, especially for weak base compounds with highly pH-dependent solubility. FDA has released a guidance on the evaluation of pH-mediated DDI assessments using in vitro testing and clinical studies. Currently, there is no common practice of ways of testing across the academia and industry. The development of biopredictive method and physiologically-based biopharmaceutics modeling (PBBM) approaches to assess acid-reducing agent (ARA)-DDI have been proven with accurate prediction and could decrease drug development burden, inform clinical design and potentially waive clinical studies. Formulation strategies and careful clinical design could help mitigate the pH-mediated DDI to avoid more clinical studies and label restrictions, ultimately benefiting the patient. In this review paper, a detailed introduction on biorelevant dissolution testing, preclinical and clinical study requirement and PBPK modeling approaches to assess ARA-DDI are described. An improved decision tree for pH-mediated DDI is proposed. Potential mitigations including clinical or formulation strategies are discussed.

Virtual Bioequivalence Assessment of Ritlecitinib Capsules with Incorporation of Observed Clinical Variability Using a Physiologically Based Pharmacokinetic Model

Ritlecitinib, an orally available Janus kinase 3 and tyrosine kinase inhibitor being developed for the treatment of alopecia areata (AA), is highly soluble across the physiological pH range at the therapeutic dose. As such, it is expected to dissolve rapidly in any in vitro dissolution conditions. However, in vitro dissolution data showed slower dissolution for 100-mg capsules, used for the clinical bioequivalence (BE) study, compared with proposed commercial 50-mg capsules. Hence, a biowaiver for the lower 50-mg strength using comparable multimedia dissolution based on the f2 similarity factor was not possible. The in vivo relevance of this observed in vitro dissolution profile was evaluated with a physiologically based pharmacokinetic (PBPK) model. This report describes the development, verification, and application of the ritlecitinib PBPK model to translate observed in vitro dissolution data to an in vivo PK profile for ritlecitinib capsule formulations. Virtual BE (VBE) trials were conducted using the Simcyp VBE module, including the model-predicted within-subject variability or intra-subject coefficient of variation (ICV). The results showed the predicted ICV was predicted to be smaller than observed clinical ICV, resulting in a more optimistic BE risk assessment. Additional VBE assessment was conducted by incorporating clinically observed ICV. The VBE trial results including clinically observed ICV demonstrated that proposed commercial 50-mg capsules vs clinical 100-mg capsules were bioequivalent, with > 90% probability of success. This study demonstrates a PBPK model-based biowaiver for a clinical BE study while introducing a novel method to integrate clinically observed ICV into VBE trials with PBPK models. Trial registration: NCT02309827, NCT02684760, NCT04004663, NCT04390776, NCT05040295, NCT05128058.

Using Mechanistic Modeling Approaches to Support Bioequivalence Assessments for Oral Products

This report summarizes the proceedings for Day 1 Session 3 of the 2-day public workshop entitled "Best Practices for Utilizing Modeling Approaches to Support Generic Product Development," a jointly sponsored workshop by the US Food and Drug Administration (FDA) and the Center for Research on Complex Generics (CRCG) in the year 2022. The aims of this workshop were to discuss how to modernize approaches for efficiently demonstrating bioequivalence (BE), to establish their role in modern paradigms of generic drug development, and to explore and develop best practices for the use of modeling and simulation approaches in regulatory submissions and approval. The theme of this session is mechanistic modeling approaches supporting BE assessments for oral drug products. As a summary, with more successful cases of PBPK absorption modeling being developed and shared, the general strategies/frameworks on using PBPK for oral products are being formed; this will help further evolvement of this area. In addition, the early communications between the industry and the agency through appropriate pathways (e.g., pre-abbreviated new drug applications (pre-ANDA) meetings) are encouraged, and this will speed up the successful development and utility of PBPK modeling for oral products.

Approaches to Account for Colon Absorption in Physiologically Based Biopharmaceutics Modeling of Extended-Release Drug Products

The rate and extent of colon absorption are important determinants of the in vivo performance of extended-release (ER) drug products. The ability to appropriately predict this at different stages of development using mechanistic physiologically based biopharmaceutic modeling (PBBM) is highly desirable. This investigation aimed to evaluate the prediction performance of three different approaches to account for colon absorption in predictions of the in vivo performance of ER drug product variants with different in vitro release profiles. This was done by mechanistic predictions of the absorption and plasma exposure of the ER drug products using GastroPlus and GI-Sim for five drugs with different degrees of colon absorption limitations in humans. Colon absorption was accounted for in the predictions using three different approaches: (1) by an a priori approach using the default colon models, (2) by fitting the colon absorption scaling factors to the observed plasma concentration-time profiles after direct administration to the colon in humans, or (3) from the ER drug product variant with the slowest in vitro release profile. The prediction performance was evaluated based on the percentage prediction error and the average absolute prediction error (AAPE). Two levels of acceptance criteria corresponding to highly accurate (AAPE ≤ 20%) and accurate (AAPE 20-50%) predictions were defined prior to the evaluation. For the a priori approach, the relative bioavailability (Frel), AUC0-t, and Cmax of the ER drug product variants for the low to medium colon absorption limitation risk drugs was accurately predicted with an AAPE range of 11-53 and 8-59% for GastroPlus and GI-Sim, respectively. However, the prediction performance was poor for the high colon absorption limitation risk drugs. Moreover, accounting for the human regional colon absorption data in the models did not improve the prediction performance. In contrast, using the colon absorption scaling factors derived from the slowest ER variant significantly improved the prediction performance regardless of colon absorption limitation, with a majority of the predictions meeting the high accuracy criteria. For the slowest ER approach, the AAPE ranges were 5-24 and 5-32% for GastroPlus and GI-Sim, respectively, excluding the low permeability drug. In conclusion, the a priori PBBM can be used during candidate selection and early product design to predict the in vivo performance of ER drug products for low to medium colon absorption limitation risk drugs with sufficient accuracy. The results also indicate a limited value in performing human regional absorption studies in which the drug is administered to the colon as a bolus to support PBBM development for ER drug products. Instead, by performing an early streamlined relative bioavailability study with the slowest relevant ER in vitro release profile, a highly accurate PBBM suitable for ER predictions for commercial and regulatory applications can be developed, except for permeability-limited drugs.

Applications of Modeling and Simulation Approaches in Support of Drug Product Development of Oral Dosage Forms and Locally Acting Drug Products: a Symposium Summary

The number of modeling and simulation applications, including physiologically based pharmacokinetic (PBPK) models, physiologically based biopharmaceutics modeling (PBBM), and empirical models, has been constantly increasing along with the regulatory acceptance of these methodologies. While aiming at minimizing unnecessary human testing, these methodologies are used today to support the development and approval of novel drug products and generics. Modeling approaches are leveraged today for assessing drug-drug interaction, informing dose adjustments in renally or hepatically impaired patients, perform dose selection in pediatrics and pregnant women and diseased populations, and conduct biopharmaceutics-related assessments such as establish clinically relevant specifications for drug products and achieve quality assurance throughout the product life cycle. In the generics space, PBPK analyses are utilized toward virtual bioequivalence assessments within the scope of alternative bioequivalence approaches, product-specific guidance development, and food effect assessments among others. Case studies highlighting the evolving and expanding role of modeling and simulation approaches within the biopharmaceutics space were presented at the symposium titled "Model Informed Drug Development (MIDD): Role in Dose Selection, Vulnerable Populations, and Biowaivers - Chemical Entities" and Prologue "PBPK/PBBM to inform the Bioequivalence Safe Space, Food Effects, and pH-mediated DDIs" at the American Association of Pharmaceutical Scientists (AAPS) PharmSci 360 Annual Meeting in Boston, MA, on October 16-19, 2022, and are summarized here.

Novel application of PBBM to justify impact of faster dissolution on safety and pharmacokinetics - a case study and utility in regulatory justifications

Physiologically based biopharmaceutics modelling (PBBM) was recognised as potential approach for biopharmaceutics applications. However, PBBM to justify safety is an unexplored area.In this manuscript, we elucidated PBBM application for safety justification. Product DRL is a generic extended release tablet containing an anti-epileptic narrow therapeutic index (NTI) drug. During dossier review, regulatory agency requested to evaluate the impact of faster dissolution profiles observed during stability on safety aspects. In order to justify, PBBMbased strategy was adapted.Model was validated and population simulations were performed for reference and test formulations and the predictions matched with clinical outcome. The model was found to be sensitive to dissolution changes and hence applied for the prediction of stability batches exhibiting faster dissolution profiles, virtually generated profiles at lower and upper specifications. The maximum predicted plasma levels were well below the reported safety levels, thereby demonstrating safety of the product.Overall, a novel application of PBBM to justify safety was demonstrated. Similar justifications using PBBM and linking with safety can be adopted where safety can be impacted due to aggravated dissolution profiles. Such justifications have potential to avoid clinical safety studies and helps in faster approval of drug product.

In Silico Modeling Approaches Coupled with In Vitro Characterization in Predicting In Vivo Performance of Drug Delivery System Formulations

Optimization of the in vivo performance of dosage forms in humans is essential in developing not only conventional formulations but also drug delivery system (DDS) formulations. Although animal experiments are still useful for these formulations, in silico approaches have become increasingly important for DDS formulations with regard to species-specific differences in physiology that can affect the in vivo performance of dosage forms between animals and humans. Furthermore, it is also important to couple in vitro characterizations with in silico models to predict in vivo performance in humans precisely. In this review article, I summarized in vitro-in silico approaches to predicting the in vivo performance of oral DDS formulations (amorphous solid dispersions, lipid-based formulations, nanosized formulations, cyclodextrins-based formulations, sustained release products, enteric coat products, and orally disintegrating tablets) and parenteral DDS formulations (cyclodextrins-based formulations, liposomes, and inhaled formulations).

Best Practices for Integration of Dissolution Data into Physiologically Based Biopharmaceutics Models (PBBM): A Biopharmaceutics Modeling Scientist Perspective

Dissolution is considered as a critical input into physiologically based biopharmaceutics models (PBBM) as it governs in vivo exposure. Despite many workshops, initiatives by academia, industry, and regulatory, wider practices are followed for dissolution data input into PBBM models. Due to variety of options available for dissolution data input into PBBM models, it is important to understand pros, cons, and best practices while using specific dissolution model. This present article attempts to summarize current understanding of various dissolution models and data inputs in PBBM software's and aims to discuss practical challenges and ways to overcome such scenarios. Different approaches to incorporate dissolution data for immediate, modified, and delayed release formulations are discussed in detail. Common challenges faced during fitting of z-factor are discussed along with novel approach of dissolution data incorporation using P-PSD model. Ways to incorporate dissolution data for MR formulations using Weibull and IVIVR approaches were portrayed with examples. Strategies to incorporate dissolution data for DR formulations was depicted along with practical aspects. Approaches to generate virtual dissolution profiles, using Weibull function, DDDPlus, and time scaling for defining dissolution safe space, and strategies to generate virtual dissolution profiles for justifying single and multiple dissolution specifications were discussed. Finally, novel ways to integrate dissolution data for complex products such as liposomes, data from complex dissolution systems, importance of precipitation, and bio-predictive ability of QC media for evaluation of CBA's impact were discussed. Overall, this article aims to provide an easy guide for biopharmaceutics modeling scientist to integrate dissolution data effectively into PBBM models.

In-Depth Analysis of Physiologically Based Pharmacokinetic (PBPK) Modeling Utilization in Different Application Fields Using Text Mining Tools

In the past decade, only a small number of papers have elaborated on the application of physiologically based pharmacokinetic (PBPK) modeling across different areas. In this review, an in-depth analysis of the distribution of PBPK modeling in relation to its application in various research topics and model validation was conducted by text mining tools. Orange 3.32.0, an open-source data mining program was used for text mining. PubMed was used for data retrieval, and the collected articles were analyzed by several widgets. A total of 2699 articles related to PBPK modeling met the predefined criteria. The number of publications per year has been rising steadily. Regarding the application areas, the results revealed that 26% of the publications described the use of PBPK modeling in early drug development, risk assessment and toxicity assessment, followed by absorption/formulation modeling (25%), prediction of drug-disease interactions (20%), drug-drug interactions (DDIs) (17%) and pediatric drug development (12%). Furthermore, the analysis showed that only 12% of the publications mentioned model validation, of which 51% referred to literature-based validation and 26% to experimentally validated models. The obtained results present a valuable review of the state-of-the-art regarding PBPK modeling applications in drug discovery and development and related fields.